Will it form a glass? Tackling glass formation using binary classification

TL;DR

This study uses machine learning to predict glass formation in inorganic liquids, achieving high accuracy and providing insights that align with and extend existing scientific understanding.

Contribution

It introduces a binary classification approach with random forests for glass formation prediction, including rigorous model selection and interpretability analysis.

Findings

Random forest classifiers achieved ROC-AUC ~0.89 and PR-AUC ~0.95.

SHAP analysis revealed the positive correlation between bandgap energy and glass formation.

Adding stability parameters did not improve performance but reduced model complexity.

Abstract

Glass formation is one of the most important and fundamental open problems in glass science. Predicting whether a liquid can be easily frozen into a glass appears simple but is far from it. In this communication, we address glass formation in inorganic nonmetallic liquids using binary classification to predict the probability that a given liquid will form a glass under typical laboratory conditions. Using a dataset of more than 50,000 examples, we trained random forest classifiers that achieved ROC-AUC values around 0.89 and PR-AUC close to 0.95 on the holdout dataset (i.e., unseen data). A rigorous model selection routine was employed, including hyperparameter tuning with cross-validation, and four different data treatment routes were evaluated. Using SHAP values, we extracted valuable insights from the trained models that both agree with established knowledge and extend it. For example, we identified that the bandgap energy of the constituent chemical elements is positively correlated with glass formation. When glass stability parameters and Jezica were added to the dataset, no performance improvement was observed, but model complexity decreased significantly. This result is particularly relevant for composition screening, especially in inverse design problems.